Bernal S D_2010.pdf - University of Plymouth

6.1. ANALYSIS OF RESULTS
different objects (false negative), leading to reduced invariance.
The graph in Figure 5.16 indicates that the S2 RF size also affects recognition performance but
has different effects for each of the distorted test sets. The occluded test set works best with the
smallest S2 RF size, probably because it better captures ihe non-occluded parts of the object,
whereas the bigger RF sizes tend to include more occluded sections. Bigger RF sizes show a
slight advantage when recognizing scaled objects, as the difference in size is less accentuated
within large RFs, while it may lead to radically different smaller-sized features. Overall, it is
clear that the best results are obtained by averaging over the four different S2 RF sizes, as one
size's shortcomings are compensated for by another one's strengths.
Finally, a comparison between different models is shown in Figure 5.17, demonstrating that
the proposed Bayesian network can achieve similar feedforward categorization results to the
original HMAX model. Note that the comparison is only rigorously valid between the HMAX
model and the Bayesian Belief Propagation (BBP) 3-level model, as these have equivalcnl num­
bers of layers, nodes and features per layer. The alternative BBP 3-level Yamane version was
specitically modilied. by reducing the pooling region and increasing the number of nodes ol" the
lop layers, to improve the categorization of the translated lest set. Implementing the same mod­
ifications in the original HMAX miKlel would, presumably, yield belter results itian the BBP
version, in the same way that the original 3-level HMAX version produces better results than
the 3-level BBP model.
The superior results of HMAX are, however, not surprising as it was specitically designed
to perform feedforward categorization and employs more exact and sophisticated operations,
namely the max and the Radial Basis function, than the BBP model. In fact, it is remarkable that
the BBP miKlel can achieve comparable categorization results using the local belitfpropagation
operations, namely a weighted product operation for selectivity and a weighted sum operation
for invariance. Crucially, using the same algorithm and structure, the BBP model also achieves
recursive feedback modulation, which has been pinpointed as the major limitation of HMAX
(Serre et al. 200.*ia).
With respect to the HTMIike model, as was previously noted, the Numenta Vision Toolkit was
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